Prospective Hindsight: Self-Calibrating Reinforcement Learning via Prediction-Reality Gaps
The paper’s core move is to train on the gap between what an agent expected before feedback and how the outcome looked afterward.
Prospective Hindsight adds that “surprise” signal to existing reinforcement learning methods, weighting rollouts where the agent’s self-model was most wrong. Because the predictor shares parameters with the policy, the focus shifts as the model’s blind spots change. The authors report gains in both task performance and calibration across single-turn verifiable tasks and a multi-turn personal-agent task, under GRPO, on-policy distillation, and a combined setup. HF Daily Papers' note
Prospective Hindsight adds that “surprise” signal to existing reinforcement learning methods, weighting rollouts where the agent’s self-model was most wrong. Because the predictor shares parameters with the policy, the focus shifts as the model’s blind spots change. The authors report gains in both task performance and calibration across single-turn verifiable tasks and a multi-turn personal-agent task, under GRPO, on-policy distillation, and a combined setup. HF Daily Papers' note
score 5